{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model = tf.keras.models.load_model(\"./IAI_IMS_pretrained_model_final.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_normal = glob.glob(\"D:/matlab/ims/normal/*\")\n",
    "files_inner = glob.glob(\"D:/matlab/ims/inner/*\")\n",
    "files_outer = glob.glob(\"D:/matlab/ims/outer/*\")\n",
    "files_ball = glob.glob(\"D:/matlab/ims/ball/*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, train_normal = train_test_split(files_normal, test_size = 50, random_state = 23)\n",
    "_, train_inner = train_test_split(files_inner, test_size = 50, random_state = 34)\n",
    "_, train_outer = train_test_split(files_outer, test_size = 50, random_state = 232)\n",
    "_, train_ball = train_test_split(files_ball, test_size = 50, random_state = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_files = train_normal + train_inner + train_outer + train_ball\n",
    "len(train_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_files = files_normal[650:750] + files_inner[650:750] + files_outer[650:750] + files_ball[650:750]\n",
    "len(test_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_data(file_names):\n",
    "    data = []\n",
    "    labels = []\n",
    "    patterns = tf.constant([\".*(normal)\", \".*(inner)\", \".*(outer)\", \".*(ball)\"])\n",
    "    for file in file_names:\n",
    "        temp = pd.read_csv(open(file,'r'), sep = \"\\s+\", header = None)\n",
    "        fault_columns = [0, 4, 2, 6]              # In this order, 0-normal, 4-inner, 2-outer, 6-ball\n",
    "        num = np.int(np.floor(len(temp[0])/1024)) # As all columns have same number of entries\n",
    "        j = 0\n",
    "        for pattern in patterns:\n",
    "            if re.match(pattern.numpy(), tf.constant(file).numpy()):\n",
    "                labels = labels + list(np.repeat(j,num)) \n",
    "                column_number = fault_columns[j]\n",
    "                break\n",
    "            j = j + 1   \n",
    "        data = data + list(temp[column_number][0:num*1024].values.reshape(num,32,32,1))\n",
    "\n",
    "    data = np.asarray(data).reshape(-1,32,32,1)\n",
    "    labels = np.asarray(labels)\n",
    "    return data, labels  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4000, 32, 32, 1) (4000,)\n"
     ]
    }
   ],
   "source": [
    "train_data, train_labels = create_data(train_files)\n",
    "print(train_data.shape, train_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8000, 32, 32, 1) (8000,)\n"
     ]
    }
   ],
   "source": [
    "test_data, test_labels = create_data(test_files)\n",
    "print(test_data.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 16)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs_train = my_model(train_data)\n",
    "outputs_train = outputs_train.numpy()\n",
    "outputs_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 16)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs_test = my_model(test_data)\n",
    "outputs_test = outputs_test.numpy()\n",
    "outputs_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = np.random.permutation(len(outputs_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs_train, train_labels = outputs_train[index], train_labels[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_fit = SVC(C = 1, kernel = \"rbf\", gamma = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_fit.fit(outputs_train, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_res = svm_fit.predict(outputs_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1000,    0,    0,    0],\n",
       "       [   0, 1000,    0,    0],\n",
       "       [   0,    0, 1000,    0],\n",
       "       [   1,    0,    0,  999]], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(train_labels, train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_res = svm_fit.predict(outputs_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1998,    0,    0,    2],\n",
       "       [   0, 1998,    2,    0],\n",
       "       [   0,    0, 2000,    0],\n",
       "       [   0,    1,    0, 1999]], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat = confusion_matrix(test_labels, test_res)\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "confu = sns.heatmap(mat, cmap = \"Blues\",annot = True, fmt = \"d\", cbar = False,\n",
    "                    xticklabels = [\"Normal\", \"Inner\", \"Outer\", \"Ball\"],\n",
    "                    yticklabels = [\"Normal\", \"Inner\", \"Outer\", \"Ball\"],\n",
    "                    linewidths = 0.1, linecolor = \"lightblue\")\n",
    "confu.set(xlabel = \"Predicted\", ylabel = \"True\")\n",
    "confu.set_yticklabels(confu.get_yticklabels(),va = \"center\", rotation = 90)\n",
    "plt.savefig(\"confusion_matrix_ims_iai.png\", bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999375"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(test_labels, test_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "fault_type = pd.Categorical(np.repeat([\"Normal\", \"Inner\", \"Outer\",\"Ball\"],2000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne_res = TSNE(n_components=2,random_state=4).fit_transform(outputs_test)\n",
    "plt.figure()\n",
    "sns.scatterplot(\n",
    "        x=tsne_res[:,0], y= tsne_res[:,1],\n",
    "        hue= fault_type,\n",
    "        data=pd.DataFrame(tsne_res))\n",
    "plt.legend(title = \"Fault Type\")\n",
    "plt.xlabel(\"First t-SNE Component\")\n",
    "plt.ylabel(\"Second t-SNE Component\")\n",
    "plt.savefig(\"tsne_test_ims_iai.png\", bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count number of nonzero activations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0.,    0.,  355., 2000.,    0.,    0.,  129.,    0.,  167.,\n",
       "        226., 2000.,  174.,  288.,  198.,   30.,    0.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = np.zeros((16,))\n",
    "for i in np.arange(2000):\n",
    "    for j in range(16):\n",
    "        if outputs_test[i,j] != 0:\n",
    "            freq[j] = freq[j] + 1\n",
    "freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.022e+03, 0.000e+00, 4.420e+02, 2.000e+03, 1.501e+03, 1.747e+03,\n",
       "       0.000e+00, 0.000e+00, 3.000e+00, 9.650e+02, 2.750e+02, 1.973e+03,\n",
       "       2.000e+03, 1.000e+00, 2.000e+03, 1.998e+03])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_1 = np.zeros((16,))\n",
    "for i in np.arange(2000,4000):\n",
    "    for j in range(16):\n",
    "        if outputs_test[i,j] != 0:\n",
    "            freq_1[j] = freq_1[j] + 1\n",
    "freq_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2000.,    0., 2000., 2000., 2000., 2000., 1995.,    0.,   42.,\n",
       "       2000.,   33., 2000., 1980.,  172., 2000., 2000.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_2 = np.zeros((16,))\n",
    "for i in np.arange(4000,6000):\n",
    "    for j in range(16):\n",
    "        if outputs_test[i,j] != 0:\n",
    "            freq_2[j] = freq_2[j] + 1\n",
    "freq_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1981.,    0., 2000., 2000.,  755., 1959., 1999.,    0., 2000.,\n",
       "       2000., 2000., 2000.,  342., 1999., 1989., 1758.])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_3 = np.zeros((16,))\n",
    "for i in np.arange(6000,8000):\n",
    "    for j in range(16):\n",
    "        if outputs_test[i,j] != 0:\n",
    "            freq_3[j] = freq_3[j] + 1\n",
    "freq_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_check = pd.DataFrame(data = {'freq':freq, 'freq_1':freq_1, 'freq_2':freq_2, 'freq_3':freq_3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (3,8))\n",
    "heatmap = sns.heatmap(df_check.to_numpy().astype(np.int32), cmap = \"Blues\", fmt = \"d\", annot = True, cbar = False,\n",
    "                      xticklabels = [\"Normal\", \"Inner\", \"Outer\", \"Ball\"], yticklabels = np.arange(1,17),\n",
    "                      linewidths = 0.1, linecolor = \"lightblue\")\n",
    "heatmap.set_yticklabels(heatmap.get_yticklabels(), rotation = 0)\n",
    "heatmap.set(xlabel = \"Fault Type\", ylabel = \"Frequency of nonzero activations at a neuron\")\n",
    "plt.savefig(\"activation_freq_ims_iai.png\", bbox_inches = \"tight\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow_env",
   "language": "python",
   "name": "tensorflwo_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
